In this project, we leverage the power of the BERT (Bidirectional Encoder Representations from Transformers) model for fine-tuned multi-category text classification in the context of E-commerce. Our dataset comprises product descriptions from four distinct categories - "Electronics," "Household," "Books," and "Clothing & Accessories." The goal is to develop a robust classification model that accurately categorizes product descriptions into these diverse E-commerce segments.
Through the fine-tuning process, we train the BERT model to capture nuanced features and context-specific information present in E-commerce texts. This project offers valuable insights into the challenges and opportunities associated with multi-category classification in the E-commerce domain. By unraveling the complexities of product descriptions, our model provides a foundation for improved E-commerce catalog organization, search functionality, and user experience.
#Dataset Link:https://www.kaggle.com/datasets/saurabhshahane/ecommerce-text-classification